Truvian raises $105 million to commercialize its automated blood-testing platform

Truvian

San Diego, California-based Truvian, a startup developing blood-testing technology that leans heavily on automation, today announced the close of an over $105 million oversubscribed series C round led by TYH Ventures, 7wireVentures’ Glen Tullman, and Wittington Ventures. Truvian says the funds will advance development of its benchtop blood testing system and enable the company to grow its product development team.

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Artificial Intelligence for Automated and Autonomous discovery of better battery materials

AI Autonomous Discovery

While applications such as electric mobility, stationary storage, drones and medical implants continue to take off, the global demand for sustainable rechargeable batteries is expected to increase drastically in the next decade. Europe alone would need a cell production capacity of at least 200 GWh up to the TWh range.

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Scaling for Robot Intelligence

Robot intelligence

RIOS CorporationJust now·3 min readBy Matt ShafferTechnologically, the last 30 years or so have been shaped by advancements in computation, and the ability to build machines that can make decisions independent of human operators is a direct result of this progress. With the growing global demand for machines that can perform labor, intelligent automation will bring about the real changes needed to deliver at scale. Though historically, robotic systems with embedded intelligence are inherently more difficult to build with reliability because they operate in the real world — a world with less regularity and more unpredictable consequences than the carefully-designed frameworks of the digital world. Given the challenges, it is not surprising to consider that factory automation is still largely driven by human workers who perform tasks that are often repetitive, but difficult to automate.Machine learning is most effective at scale, where the experiences of many systems can be aggregated.Automation is non-trivial, but it is not due to the fact that research cannot solve a lot of these problems — but that it only became a possibility more recently. There are certainly quite a few reasons for this, some of which have to do with the hardware and computational advancements, and others that revolve around data. But there is another interesting theory going around that is articulated by Sara Hooker in “The Hardware Lottery”. She postulated that research directions in the field of machine learning are often explored due to software and hardware available at the time, rather than being motivated by the most promising ideas. This theory is aligned with our premise at RIOS that advancing the capabilities of robots is heavily dependent on both specialized hardware and software that must coevolve.Robots in the real world have traditionally been programmed in isolation on a single task, rather than leveraging collective knowledge as in simulated environments..Today, we are reaching an inflection point, and there is a monumental opportunity to develop custom hardware and software systems that enable robots to take on increasingly open-ended tasks without the need of reprogramming for each new instruction. We can do this by taking the lessons of the internet to apply data at scale to robotics. By strategically designing systems with the intent of learning from them, and building the infrastructure to support information sharing, we can adapt more quickly to new tasks and master the ones we are already familiar with. The real promise of applying machine learning to robotics is not teaching a single robot to learn for itself, but to aggregate experience from a vast network of robots so that they can improve at scale.A core tenet of what we do at RIOS is to build machines with this idea in mind. Like hardware, skills and behaviors should be transferable across platforms when possible, and each deployed system should be able to share what it has learned with other systems. At a high level, you can think of this as storing knowledge rather than just data to reduce the need for retraining. The result is a class of robots that can do a variety of tasks and address new challenges with less development time. By building distributed robots that continuously learn from both their environment and the collective experience of others, we can help push intelligent robotics forward at scale much in the same way that the information economy benefited from the web.The next generation of technological progress is starting to favor organizations that can rapidly assemble the best technologies of the web-era and use them to take fields like robotics in new directions. In many parts of the world, where labor shortages exist or workers are subjected to poor conditions, this couldn’t come at a better time. Moreover, what used to be a long lead-time in deploying new systems or developing solutions is disappearing as robots can reuse not just hardware, but prior knowledge when taking on new tasks. As more robots fill empty roles in factories, we’ll start wonder how we lived without them, and eventually forget they are doing the most thankless of work for us without any complaints.Matt Shaffer is the RIOS Director of Artificial Intelligence and is the architect behind the brain of our robots. This article is a shortened version of Matt’s article Scaling Artificial Intelligence for Robotics in 2021.

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Locus Robotics raises $150 million to scale its warehouse robotics platform

Locus Robotics

Locus Robotics, a Wilmington, Massachusetts-based warehouse robotics startup, today announced it has raised $150 million in series E funding at a $1 billion post-money valuation. The company says the funding will allow it to accelerate product innovation and global expansion. Locus expects that in the next four years, over a million warehouse robots will be installed and that the number of warehouses using them will grow tenfold.

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SEER: Simulative Emotional Expression Robot

SEER Robot

“SEER” is a humanoid robotic head developed as an artistic work by Takayuki Todo. It explores the significance of gaze and facial expression in the sphere of human-machine research. Takayuki Todo is interested in how people establish an emotional relationship with humanoid robots. As the discipline of robotics has shown for years, a realistic similarity to the human form alone is not able to break down the distance between a human and a machine.

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The origin of Robot Arm Programming Languages

Robot Arm

This short blog post is about the origin of languages for describing tasks in automation, in particular for industrial robot arms. Three people who have passed away, but were key players were Doug Ross, Victor Scheinman, and Richard (Lou) Paul, not as well known as some other tech stars, but very influential in their fields. Here, I must rely not on questions to them that I should have asked in the past, but from personal recollections and online sources.

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How to use self-directed learning when onboarding front-line workers

2 employess

In a volatile market, businesses need to remain agile. Part of this is down to having robust processes to onboard new employees efficiently. Here, we’ll look at how learning and development managers can empower employees to undertake self-directed learning and improve outcomes for workplace reinforcement and knowledge retention.

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Artificial Intelligence — Agents and Environments

Agents - Environments

Agent and Environment are two pillars in Artificial Intelligence, our aim is to build intellectual agents and work in an environment. If you consider broadly agent is the solution and environment is the problem. In simple terms, even starter or researcher can understand that and is defined Agent as game and Environment as ground.

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How RPA and Intelligent Automation contribute to employee success

Group of people standing

Robotic process automation (RPA) is an extremely valuable tool for businesses across the industry spectrum, and this claim is well substantiated. A Deloitte study, for instance, shows that an impressive 86% of companies that have integrated RPA into their processes increased organisational efficiency and productivity. And this is certainly not all.

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Slync.io raises $60 million to automate supply chain processes

Truck parked

Slync.io, a shipping and logistics process automation company, today announced it has closed a $60 million series B funding round. The company says it will leverage the investment to continue serving its customers, grow its physical presence in Europe and Asia, and expand its core teams.

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How AI is redefining RPA

Gartner has identified Robotic Process Automation to be the fastest-growing segment of the global enterprise software market, predicting RPA software revenue to reach $1.89 billion in 2021. It is also one of the few technologies to gain significant traction during the pandemic and rapidly continues to expand in 2021. A key contributor to its rapid growth could be its convergence with another powerful technology: Artificial Intelligence (AI).

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Opinion: What is real intelligent automation?

Model brain

Intelligent automation reduces costs, improves efficiency and allows businesses to initiate change through technology. When applied to business operations or customer services, it has proven to be an invaluable piece of technology as it improves productivity thus saving time in the process with quicker responses. With intelligent automation, manufacturing giant Siemens has driven 10 times…

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Quali raises $54 million to automate cloud management and deployment

Quali, a company developing sandbox software for cloud and DevOps automation, today announced it has raised $54 million. The company says the funds will be put toward expanding its customer base and enabling new partnerships while growing its workforce.

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That is not intelligence

Let’s start by saying that perhaps 99% of what’s called AI these days, even the most fashionable ‘deep learning’ — merely amounts to a vastly complicated statistical analysis of humongous amounts of data enabled by the unprecedented drop of the costs of storage and compute (bytes/$ and FLOPS/$ respectively).

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